Production Forecasting in Shales: A Comparative Field Data Study Using Large Well Counts

Abstract

As the effort to develop unconventional reservoirs takes on a greater pace within the industry, so too does the effort to understand the way in which these unique reservoirs produce. Classical forecasting techniques such as Arps are typically not able to adapt to the characteristic "tail" exhibited by wells producing from these reservoirs. As such, a tremendous amount of investigation and research has been undertaken to better understand and therefore better predict future performance. As a result the literature is full of alternative techniques. In this paper we explore a number of these techniques, comparing them to the classical Arps method as a form of benchmark. In addition, we take a "backcasting" approach on several large shale fields, including the Barnett, Eagle Ford, Bakken and Montney. This approach is equivalent to going back in time, hiding later production data, forecasting and comparing forecasts to what we now know production rates to have been. We also test the applicability of classical and newer forecasting techniques on big data sets by identifying changes in operating conditions and calculating decline rates for the period thereafter. As seen in this study, each method has its own "sweet spot" in which it yields accurate results. This study differs from other published studies in that our focus was not so much on obtaining Estimated Ultimate Recovery (EUR) values that matched the actual production of depleted wells, but rather was on predicting production rates in the relatively near term. While EUR estimation is a key performance indicator, it falls into the domain of the long term planners. Conversely, future production rates are of prime importance to operational personnel and to their short term decision making processes, such as timing of infill drilling and remedial activity, along with surface facility planning activities such as system upgrades or reconfiguration. Another aspect of this study which differs from others published is that we did not preclude wells with noisy data. Rather, we embraced the challenge presented by real data sets. In this way we could ensure that our proposals could be applied universally and not simply to those wells with well-behaved production histories.

Continuous‐time formulations for the optimal planning of multiple refracture treatments in a shale gas well

“…On the other hand, the NLP model is nonconvex, thus requiring global solvers to ensure global optimality. Recent studies also suggest that alternative forecast techniques (other than hyperbolic) could be a more accurate choice for certain shale wells . We plan to extend the continuous‐time model to account for more complex forecast functions in future work.…”

“…In essence, the decreasing power function adopted by Cafaro et al 10 is an adaptation of the hyperbolic equation for general decline in a well, first proposed by Arps. 12 For a thorough analysis on the value and suitability of hyperbolic declines in shale gas reservoirs we refer the reader to the recent work of Valentine et al 13 These authors also focus on the importance of refracture timing to forecast shale gas production. However, parameters k and a by themselves can only represent the production of a shale well after a single intervention.…”